Title :
Static calibration of transducers using parametrization and neural-network-based approximation
Author :
Kluk, P. ; Morawski, R.Z.
Author_Institution :
Inst. of Radioelectron., Warsaw Univ. of Technol., Poland
Abstract :
The problem of static calibration of transducers converting a static measurand into a function is considered. The neural-network-based methodology is developed and examined using synthetic data representing a real biosensor. The important part of this methodology is compression of the calibration data
Keywords :
biosensors; calibration; computational complexity; data compression; discrete Fourier transforms; feedforward neural nets; inverse problems; parameter estimation; sensitivity analysis; sensors; signal reconstruction; transducers; complexity; data compression; discrete FT; feedforward neural net; inverse operator; neural-network-based approximation; operator identification; parametrization; real biosensor; sensitivity; static calibration; static measurand; synthetic data; transducers; Biosensors; Calibration; Current measurement; Displacement measurement; Neural networks; Neurons; Q measurement; Time measurement; Transducers; Vectors;
Conference_Titel :
Instrumentation and Measurement Technology Conference, 1996. IMTC-96. Conference Proceedings. Quality Measurements: The Indispensable Bridge between Theory and Reality., IEEE
Conference_Location :
Brussels
Print_ISBN :
0-7803-3312-8
DOI :
10.1109/IMTC.1996.507449
